National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Conference Paper 

NREL/CP-620-38139 

May 2005 

Reduced Form of Detailed 

Modeling of Wind Transmission 

and Intermittency for Use in 

Other Models 

Preprint 

N. Blair, W. Short, and D. Heimiller 

To be presented at WindPower 2005 

Denver, Colorado 

May 15–18, 2005 
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1

Reduced Form of Detailed Modeling of Wind Transmission and 

Intermittency for Use in Other Models 

WINDPOWER 2005 

Walter Short, Nate Blair, Donna Heimiller 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

walter_short@nrel.gov

This paper, to accompany the corresponding poster, presents reduced-form outputs from the 

Wind Deployment Systems Model (WinDS) that can be implemented in other models with lessdetailed wind power modeling capabilities. Other models will be able to use the reduced-form 

results to improve the accuracy of the wind power portion of their models. 

WinDS is a multiregional, multitime-period, Geographic Information System (GIS) and linear 

programming model of capacity expansion in the electric sector of the United States. WinDS is 

designed to address the market issues related to the penetration of wind power into the electric 

sector. These principal market issues include the geographic dependency of the resource, access 

to and cost of transmission, and the intermittency of wind power. 

WinDS was used to create a supply curve of wind power transmission cost as a function of 

national wind power capacity growth by exercising the model with and without including the 

costs associated with the differences between the WinDS model and a one-region model. This 

supply curve can be used by other capacity-expansion models (assuming the same representation 

of wind as the one-region model) to obtain an accurate cost of wind capacity, even though the 

model doesn’t contain the same extensive geographical information as WinDS. 

These supply curves also provide the electric-sector community with an estimate of the 

additional costs associated with wind beyond just wind capital and operating costs. 

Section 1: WinDS Overview

WinDS is a computer model that optimizes the regional expansion of electric generation and 

transmission capacity in the continental United States over the next 50 years. To do this, it 

employs a Geographic Information System (GIS) to develop region-specific data for input to a 

linear program (LP). Most of the methodology description that follows addresses the linearprogram portion of the model and is simply referred to as WinDS. Where it is important to 

distinguish that which is done in the GIS from the LP, the GIS capability is specifically 

identified. 

WinDS minimizes system-wide costs of meeting electric loads, reserve requirements, and 

emission constraints by building and operating new generators and transmission in 26 two-year 
2

periods from 2000 to 2050. The primary outputs of WinDS are the amount of capacity and 

generation of each type of prime mover—coal, gas combined cycle, gas combustion turbine, 

nuclear, wind, etc.—in each two-year period. Figure 1 shows an example of WinDS capacity 

estimates for the United States for different generation technologies over the next 50 years. 

0

500

1000

1500

2000

2500

3000

2000 2010 2020 2030 2040 2050

Forecast Years

National Capacity (GW)

Total Wind

Coal-new

Coal-no scrub

Coal-w/scrub

Gas-CT

nuclear

Gas-CC

Hydro

oil-gas-steam

Figure 1. Base Case WinDS Capacity Estimates 

While WinDS includes all major generator types, it was designed primarily to address the market 

issues of greatest significance to wind—transmission and intermittency. The WinDS model 

examines these issues primarily by using a much higher level of geographic disaggregation than 

other models. As Figure 2 represents, WinDS uses 358 different regions in the continental 

United States. Much of the data inputs to WinDS are tied to these regions and derived from a 

detailed GIS model/database of the wind resource, transmission grid, and existing plant data. The 

geographic disaggregation of wind resources allows WinDS to calculate transmission distances, 

as well as the benefits of dispersed wind farms supplying power to a demand region. 
3

Figure 2. Regions within WinDS 

As shown in Figure 3, WinDS disaggregates the wind resource into five classes ranging from 

Class 3 (5.4 meters/second at 10 meters above ground) to Class 7 (>7.0 m/s). WinDS also 

includes offshore wind resources and distinguishes between shallow and deep offshore wind 

turbines. Shallow-water turbines are assumed to have lower initial costs, because they employ a 

solid tower with an ocean-bottom pier; while deep-water turbines are assumed to be mounted on 

floating platforms tethered to the ocean floor. For the current analysis, offshore wind was 

disabled in both the WinDS model and the one-region model (described below). 
4

Figure 3. Wind Resources in WinDS 

These different classes and types of wind have different costs and performance characteristics. 

Generally the higher wind-class sites (i.e. Class 7), are the preferred sites. However, Figure 4

shows that, at any given point in time, the wind turbines installed will be at a mix of sites with 

different wind resource classifications. This occurs because, in selecting the installation sites, 

WinDS considers not only the resource quality, but also factors such as transmission availability, 

costs and losses, correlation of the wind output with neighboring sites, environmental exclusions, 

site slope, and population density. 
5

Figure 4. Wind Capacity Results by Type and Class 

WinDS is also disaggregated over time —not only with the 26 two-year periods between 2000 

and 2050, but also within each year. Each year is divided into four seasons with each day of each 

season divided into four diurnal time slices. These 16 time slices during each year allow WinDS 

to capture the intricacies of meeting peak electric loads, both with conventional sources and 

intermittent wind generators. 

WinDS models the major conventional electricity generators, including: 

- pulverized coal 

- integrated gasification combined-cycle coal 

- existing unscrubbed coal boilers 

- existing scrubbed coal boilers 

- natural gas combined cycle 

- natural gas combustion turbines 

- nuclear 

- hydroelectricity 

Fuel costs are exogenously specified over time by NERC region, as are electric loads. WinDS is 

a national electric capacity expansion model, not a general equilibrium model. Assessing the 

potential of wind energy under any given scenario requires that the scenario be exogenously 

specified in terms of fuel costs and electric loads by NERC region over the 50-year time horizon 

of WinDS. 

While the focus of WinDS is on wind-energy technologies, the model does include some detail 

on other generation technologies. For example, there are four types of coal-fired power plants 

within WinDS—existing boilers without SO2 scrubbers, existing with scrubbers, new advanced 

0

5

10

15

20

25

30

35

2000 2010 2020 2030 2040 2050

GW

Onshore-old lines-class3

Onshore-old lines-class4

Onshore-old lines-class5

Onshore-old lines-class6

Onshore-old lines-class7

Onshore-new lines-class3

Onshore-new lines-class4

Onshore-new lines-class5

Onshore-new lines-class6

Onshore-new lines-class7 

Offshore shallow-old lines-class3

Offshore shallow-old lines-class4

Offshore shallow-old lines-class5

Offshore shallow-old lines-class6

Offshore shallow-old lines-class7

Offshore shallow-new lines-class3

Offshore shallow-new lines-class4

Offshore shallow-new lines-class5

Offshore shallow-new lines-class6

Offshore shallow-new lines-class7

Offshore deep-old lines-class3

Offshore deep-old lines-class4

Offshore deep-old lines-class5

Offshore deep-old lines-class6

Offshore deep-old lines-class7

Offshore deep-new lines-class3

Offshore deep-new lines-class4

Offshore deep-new lines-class5

Offshore deep-new lines-class6

Offshore deep-new lines-class7
6

pulverized coal plants, and new integrated-gasification combined-cycle plants. These plants can 

burn either high-sulfur or, for a cost premium, low-sulfur coal. Generation by coal plants is 

restricted to base and intermediate load with cost penalties (representing ramping/spinning costs) 

if power production during peak load periods exceed production in shoulder-peak hours. Nuclear 

is considered to be base load. Combined-cycle natural-gas plants are considered capable of 

providing some spinning reserve and quick-start capability, but the primary source of peak power 

and operating reserves are combustion turbines and hydroelectricity. Hydroelectricity is not 

allowed to increase in capacity, due to resource and environmental limitations. Hydro is also 

energy-constrained, due to water resource limitations. 

WinDS tracks emissions from both generators and storage technologies of carbon, sulfur dioxide, 

nitrogen oxides, and mercury. Caps can be imposed at the national level on any of these 

emissions. Alternatively, a carbon tax can be imposed that linearly escalates to the maximum tax 

level over time. 

Section 2: Motivation

As stated above, WinDS is designed to address the issues of intermittency and geographic 

dispersion associated with wind. This focus primarily came from the observation that existing 

models were unable to address these issues in a highly rigorous way, because they do not have 

the geographic disaggregation that is in WinDS. For example, while WinDS has 356 regions in 

the continental United States, NEMS has only 13 regions, and the U.S. MARKAL model has 

only a single region. For conventional sources of electricity generation, this geographic 

approximation is adequate, because there is minimal geographic dependency. However, these 

models cannot track the geographic dispersion of the installed wind and, therefore, cannot 

estimate the transmission costs— or the true capacity value of the wind—because the penetration 

level increases over time. 

The question, therefore, remains about the best way to improve the representation of renewable, 

geographically tied power generation within models with minimal geographic regionality. 

Unfortunately, the additional computational effort needed to modify these models to contain all 

the necessary geographic information would be overwhelming and represent a major change. An 

alternative is to develop supply curves that capture the additional system costs associated with 

intermittency and transmission as the amount of installed wind increases. The developers of the 

other models can then more easily modify their models to add this cost (depending on the 

amount of installed national wind capacity) onto the base capital cost of the wind supply. As the 

conceptual graph shows in Figure 5, the additional costs associated with intermittency, wind 

transmission, and multiple wind classes add to the base system costs that are independent of both 

models. The goal of this project is to get an accurate accounting of these additional system costs 

as a function of U.S. wind capacity installed and provide that as a supply curve. 
7

National Wind Capacity

Cost ($)

WinDS Model

1-Region Model

Base System Costs - all costs common to both 

models including ongoing cost for existing (yr 

2000) electric grid and new conventionals 

(especially new coal) 

Additional system costs due to 

wind transmission, 

intermittency, multiple wind 

classes, etc.

0 100's GW

Figure 5. Conceptual Cost Graph 

Specifically, the additional system costs accounted for in the supply curve will include: 

• the transmission cost for the distance from the wind resource to the load where it is used 

• the cost of surplus wind1

• the cost of decreasing capacity value of wind, due to greater penetration within the same 

region 

• the cost of providing operating reserve for the wind 

• the cost for changes to the conventional generation and capacity, due to these differences 

in treatment of wind power 

• the difference in cost between having only a single class of wind versus having five 

different classes of wind with different performance. 

 

1

 Surplus wind generation is defined here as the wind generation that must be discarded because it exceeds the net 

load after allowing for “must-run” conventional generation from nuclear and base-load coal plants. 
8

Section 3: Methodology

In a normal run of WinDS, the first two-year period (1999-2000) is optimized with a linear 

program, parameters are updated for the next two-year period (2001-2002), and that next period 

is optimized with the linear program. This continues until all 26 two-year periods between 2000 

and 2050 have been optimized in sequence, building on one another. 

To develop the supply curve, an additional step is added to this process for each period. Once a 

period has been optimized, as just described for a normal WinDS run, it is immediately 

reoptimized to represent wind power as it would be represented in a one-region national model. 

The difference in the total system costs between the two optimizations for the same period 

represents the differences in costs between WinDS and a one-region model that can’t capture the 

intermittency, transmission, and wind class effects. 

Dividing the difference in total system costs by the amount of wind capacity (in kW) added in 

the normal WinDS run for that period yields the marginal additional system cost per kW of wind. 

This is the value to be shown in the supply curve. Typically, this additional system cost per kW 

of wind will increase in later periods as the wind sites added are not as attractive as the wind 

sites added initially, e.g. they are further from existing transmission lines, they are lower-quality 

wind resources, and they don’t contribute as much to system reserve requirements (because their 

output can be coincident with that of wind farms already installed). 

To be sure that the differences in the system costs are due only to wind, a constraint is imposed 

on the one-region model optimization for the period that ensures that the electricity delivered to 

each PCA from new wind installations is the same as that in the normal WinDS optimization for 

that period. Thus, in both optimizations, the generation delivered by conventional power sources 

is identical in each PCA. This does not mean that the total cost associated with conventional 

power will be the same in the two optimizations. For example, more conventional capacity (as 

opposed to generation) is required in the normal WinDS optimization. because the marginal 

capacity value of a new wind farm decreases as more wind is deployed. Conventional peaking 

plants must be built in the normal WinDS optimization to compensate for this decrease in windcapacity value. 

In applying the supply curve developed by this process, care must be taken to ensure that costs 

associated with wind are not counted twice. More specifically, any model applying this wind 

supply curve must make the same assumptions as our one-region model optimizations. More 

specifically, any model applying this supply curve to capture the additional system costs 

associated with wind must not already include: 

a. Wind transmission costs (either on existing lines or new lines) 

b. Wind transmission losses (either on existing or new lines) 

c. Cost penalties for building wind farms on steep terrain and/or populated 

d. Penalties for wind capacity value below the average capacity2

e. Operating reserve additions required by wind capacity 

f. Surplus wind generation. 

 

2

 Average capacity is defined here as the nameplate capacity times the annual capacity factor of the wind farm. 
9

g. Varying costs and capacity factors for different classes of wind resources. The 

supply curves presented here assume the one-region model will model all wind 

costs as Class 5 costs. 

Section 4: Discussion of Results

Figure 6 demonstrates the change in total system costs for each period between the standard 

WinDS model optimization and the one-region model optimization. As demonstrated, the 

cost difference is highest during the period from 2012 to 2032, indicating that the largest 

impact of the wind transmission, intermittency, and other differences is during this period. 

However, this graph is somewhat misleading because it is not normalized for the amount of 

wind capacity added during each period. 

Cost Difference ($) For Each Period

-2.E+09

-1.E+09

0.E+00

1.E+09

2.E+09

3.E+09

4.E+09

5.E+09

6.E+09

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

Simulation Year

$

Figure 6: The Cost Difference ($) for Each Simulation Period 

To correct this, Figure 7 shows the basic supply curve that plots the marginal cost ($/kW) of 

new wind against the amount of total installed national wind capacity. This is the supply curve to 

be used in a one-region model. This graph represents the additional cost (above capital, 

installation, and operating costs) that needs to be added (or subtracted) to each additional kW of 

wind-power capacity installed in a one-region model. 
10

Supply Curve for 1-Region Model

-400

-300

-200

-100

0

100

200

300

400

0 20 40 60 80 100 120

GW Wind Installed

Marginal Price ($/KW)

Figure 7: Supply Curve for One-Region Models without Transmission or Intermittency Impacts 

or Separate Wind Classes 

As expected, the supply curve cost increases as the wind penetration level increases. This is due 

to the need, in reality, to use wind resources that require greater transmission costs, and 

increasing penalties on wind due to intermittency impacts (greater operating reserve necessary, 

lower capacity value, potential for surplus) as the cheaper resources are consumed. This graph 

shows that for the initial increase in wind capacity, the additional cost to account for the model 

differences is negative. This negative number indicates that the first 12 GW of wind will actually 

have a negative additional cost to the system. Therefore, any model that includes only Class 5 

wind turbines and uses this supply curve will subtract a sum of roughly 220 $/KW from the 

capital cost of the initial wind-power installations. This $220/kW accounts for the fact that, in 

reality, Class 6 and 7 wind would be used for that initial wind capacity and not the average 

undifferentiated resource used in the run for the one-region model. 
11

Supply Curve for 1-Region Models

0

50

100

150

200

250

300

350

400

15 25 35 45 55 65 75 85 95 105 115

GW Wind Installed

Marginal Price ($/KW)

Conventional Costs

Conv. Transmission

Wind Transmission 

Wind Capital

Figure 8: Supply Curve for One-Region Models with Major Components Plotted3

In Figure 8, the same supply curve as in Figure 7 is represented as a stacked chart to 

demonstrate the major drivers on the overall supply curve. The line labeled “Wind Capital” 

represents the difference in cost of the wind capacity installed in the one-region model and 

the WinDS model. As shown, this is a major component of the overall cost difference. The 

level is generally increasing, because the WinDS optimization requires a greater amount of 

wind capacity for the same amount of wind generation. The WinDS optimization requires 

more capacity and thus has higher costs because: 

• The WinDS optimization gets less generation from the same amount of capacity due 

to increasing use of Class 3 and 4 wind (as opposed to representing all wind as Class 

5 in the one-region model) as the penetration level increases. 

• The WinDS optimization gets less generation for the same amount of capacity, due to 

transmission losses that are not present in the one-region model. 

• The difference in cost due to the penalty imposed on densely populated regions and 

mountainous regions in the WinDS optimization - a penalty that is not included in the 

one-region optimization 

• The possibility of wind surplus within the WinDS optimization, which is not present 

in the one-region optimization 

The next major component is the “Wind Transmission” cost. This increases throughout, as 

the WinDS optimization has to use increasingly more expensive wind that is further from the 

load, thereby increasing the transmission costs (which, again, are not considered in the one-

 

3

 Note that the X-axis starts at 15 GW. Prior to this point are a series of negative values, which do not display 

reasonably on a stacked area chart. 
12

region optimization). The value of the wind transmission cost might be less than one would 

expect based on other studies. The WinDS model (using base case assumptions) assumes 

20% line availability for existing lines and also allows the building of short lines from one 

region to the next or within a region. This allows a great deal of wind to use existing lines 

and build very short new transmission lines especially at low penetration levels. These 

features keep the cost of transmission lower than they would be without these features. 

Future work includes sensitivities to the amount of existing lines available for new capacity. 

 In contrast to the wind transmission cost difference, the “Conventional Transmission” cost 

difference is minor. Although conventional transmission is treated the same in both models, 

there is a slight difference because of the increased conventional capacity installed in the 

WinDS model. 

The final cost difference, “Conventional Costs”, is attributable to the capital and operating 

costs for conventional capacity. This difference is caused by: 

• Additional conventional capacity built in the WinDS optimization to meet additional 

operating reserve requirements imposed by wind that are not considered in the oneregion model 

• Additional conventional capacity built in the WinDS optimization to compensate as 

the capacity value per kW of wind installed decreases in the WinDS optimization as 

more wind is installed (the one-region model assumes a constant capacity value equal 

to the class 5 capacity factor). 

We have presented the supply curve in Figures 7 and 8 as a function of the cumulative wind 

capacity installed nationwide. However, to a limited extent, these supply curve costs depend 

not only on the total wind installations, but also on the fraction of load met by wind and a 

host of other conditions that vary over time. To examine the significance of these other 

factors, we conducted a sensitivity analysis in which the wind penetrated much more rapidly. 

To do this, we used the same methodology as above, but extended the production tax credit 

to 2010. Figure 9 compares the supply curve created using this fast market penetration and 

the supply curve created using the standard assumptions. Notice that only a few points are on 

the Fast-Penetration (FP) graph, indicating that the same amount of wind is installed in far 

fewer periods. The shape and cost levels are similar but not identical. There are a variety of 

factors contributing to these differences which are greatest early in the supply curve. For 

example, the difference is greatest when the FP case reaches roughly 31 GW in 2006 while 

the base case doesn’t reach that level until 2014. By 2014, there are significant differences in 

wind cost, conventional fuel costs, electricity loads, and reserve margin requirements 

between these cases. Additionally, in the FP case the amount of wind added in one period is 

significantly higher than in the base case. This creates an inaccuracy in the FP case as the 

values used for wind capacity value, necessary operating reserve, wind surplus, etc. are all 

based on the wind level at the end of the preceding period. Therefore, the more moderate 

penetration rate found in the base case is the more accurate one to use. 
13

Supply Curves 

-400

-300

-200

-100

0

100

200

300

400

0 20 40 60 80 100 120

GW Wind Installed

Marginal Price ($/KW)

Total Marginal Cost - Fast Penetration

Total Marginal Cost - Basecase

Figure 9: Comparison of Supply Curves for One-Region models with Typical Market-Penetration 

Rate and Fast Market-Penetration Rate 

Section 5: Next Steps

First, the WinDS model itself is continually being improved. New methodologies and 

improvements to the treatment of transmission and intermittency for wind are being added 

currently and are planned. Therefore, as the WinDS model improves, improved versions of the 

reduced form outputs will also be generated. 

Hopefully, the supply curves that can be generated using the WinDS model and the methodology 

presented in this paper will be useful to other modelers as they attempt to obtain a more accurate 

representation of renewables in their models. Immediate next steps to accomplish this include: 

• Develop supply curves with the “one-region” model having multiple wind classes (but 

still no transmission costs or intermittency impacts). 

• Develop supply curves like the above, but replacing the one-region model with a number 

of large regions, e.g. the NERC regions used in NEMS 

• Work with the developers of other models to implement these results in their models. 

• Continue to document and publish these results on our WinDS Web site 

In addition to providing such supply curves for wind, we will be providing reduced form outputs 

for other renewable technologies. WinDS currently has the ability to model offshore wind, 

hydrogen production from wind, and other sources—and the ability to model concentrating solar 

power is currently being included. Supply curves for models wanting an accurate representation 

of costs associated with these technologies could be created. 
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4. TITLE AND SUBTITLE

Reduced Form of Detailed Modeling of Wind Transmission and 

Intermittency for Use in Other Models: Preprint 

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NREL/CP-620-38139 

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N. Blair, W. Short, and D. Heimiller 

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National Renewable Energy Laboratory 

1617 Cole Blvd. 

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14. ABSTRACT (Maximum 200 Words)

This paper presents reduced-form outputs from the Wind Deployment Systems Model (WinDS) that can be 

implemented in other models with less- detailed wind power modeling capabilities. Other models will be able to use 

the reduced-form results to improve the accuracy of the wind power portion of their models. WinDS is a multiregional, 

multitime-period, Geographic Information System (GIS) and linear programming model of capacity expansion in the 

electric sector of the United States. WinDS is designed to address the market issues related to the penetration of 

wind power into the electric sector. These principal market issues include the geographic dependency of the 

resource, access to and cost of transmission, and the intermittency of wind power. 

15. SUBJECT TERMS

Wind Deployment Systems Model; WinDS; GIS; wind; analysis models; wind power; production tax credit; PTC; 

renewable portfolio standard; RPS; wind resources; wind classes; wind markets; load constraints; offshore wind; 

PERI; Walter Short; Nate Blair; Donna Heimiller 

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